Density-based and classification-based methods have ruled unsupervised anomaly detection in recent years, while reconstruction-based methods are rarely mentioned for the poor reconstruction ability and low performance. However, the latter requires no costly extra training samples for the unsupervised training that is more practical, so this paper focuses on improving this kind of method and proposes a novel Omni-frequency Channel-selection Reconstruction (OCR-GAN) network to handle anomaly detection task in a perspective of frequency. Concretely, we propose a Frequency Decoupling (FD) module to decouple the input image into different frequency components and model the reconstruction process as a combination of parallel omni-frequency image restorations, as we observe a significant difference in the frequency distribution of normal and abnormal images. Given the correlation among multiple frequencies, we further propose a Channel Selection (CS) module that performs frequency interaction among different encoders by adaptively selecting different channels. Abundant experiments demonstrate the effectiveness and superiority of our approach over different kinds of methods, e.g., achieving a new state-of-the-art 98.3 detection AUC on the MVTec AD dataset without extra training data that markedly surpasses the reconstruction-based baseline by +38.1 and the current SOTA method by +0.3. Source code is available at https://github.com/zhangzjn/OCR-GAN.

翻译：近年来，基于密度和分类的方法主导了无监督异常检测领域，而基于重建的方法因重建能力不足和性能低下鲜少被提及。然而，后者无需昂贵的额外训练样本即可进行更实用的无监督训练，因此本文致力于改进这类方法，并提出一种新颖的全频通道选择重建网络（OCR-GAN），从频率视角处理异常检测任务。具体而言，我们提出频率解耦模块（FD），将输入图像解耦为不同频率分量，并将重建过程建模为并行全频图像恢复的组合，这是由于观察到正常与异常图像在频率分布上存在显著差异。考虑到多频率之间的相关性，我们进一步提出通道选择模块（CS），通过自适应选择不同通道实现不同编码器间的频率交互。大量实验证明，本方法相较于各类方法具有有效性和优越性：例如，在无需额外训练数据的情况下，在MVTec AD数据集上达到98.3%的检测AUC，较重建基线方法显著提升38.1%，较当前最先进方法提升0.3%。源代码已开源至https://github.com/zhangzjn/OCR-GAN。